Why does the classifier network in RPN output two scores?
I just want to provide this intuition 


this NN consists of a 2 steps detection pipeline (the region proposal and regression + classification in parallel) exploring a certain range of scales and aspect ratios for proposals
as the proposed region is rectangular and the objects of interests have not a rectangular appearance strictly speaking, but a mostly rectangular one (with different ARs), considering the resulting detection bboxes areas some pixels will belong to a certain relevant object appearance (e.g. pixels of a pedestrian, car, …) while other pixels in that BBox won’t be related to that semantic
as this is not semantic segmentation we can now know what pixel is actually relevant, however the objectness and backgroundness can provide a rough measure of this in an aggregated way


Let’s assume at some point in your processing pipeline, you have 2 regions containing the same car but at different scales: so ideally both of them should be associated with good confidence to CarID but it’s clear one will contain much more background pixels than the other one and you'd like this to be represented as an additional measure